E. Ventura, S. Cocco, R. Monasson, F. Zamponi

to appear in Machine Learning: Science and Technology (2024)

**
[155] Restoring balance: principled under/oversampling for optimal data classification
**

E. Loffredo, M. Pastore, S. Cocco, R. Monasson

Forty-first International Conference on Machine Learning - ICML (2024)

**
[154] Stimulation allows for reshaping network connectivity through
plasticity: a training protocol for rate models
**

F. Borra, S. Cocco, R. Monasson

Computational and Systems Neuroscience - COSYNE 2024 (2024)

**
[153] Accelerated Sampling with Stacked Restricted Boltzmann Machines
**

J. Fernandez de Cossio Diaz, C. Roussel, S. Cocco, R. Monasson

Twelth Conference on International Conference on Learning
Representations - ICLR (2024)

**
[152] Origins and breadth of pairwise epistasis in an alpha-helix of
beta-lactamase TEM-1
**

A. Birgy, C. Roussel, H. Kemble, J. Mullaert, K.
Panigoni, A. Chapron, J. Chatel, M. Magnan, H.
Jacquier, S. Cocco, R. Monasson, O. Tenaillon

submitted (2023)

**
[151] Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information
**

S.Cocco, L. Posani, R. Monasson

Accepted for publication in PNAS (April 2024)

**
[150] Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment
**

C. Malbranke, W. Rostain, F. Depardieu, S.Cocco, R. Monasson, D. Bikard

PLoS Computational Biology 19:e1011621 (2023)

**
[149] Information content in continuous attractor neural networks is
preserved in the presence of moderate disordered background
connectivity
**

T. Kuehn, R. Monasson

Phys. Rev. E 108, 064301 (2023)

**
[148] Replica method for computational problems with randomness:
principles and illustrations
**

J. Steinberg, U. Adomaityte, A. Fachechi, P. Mergny, D. Barbier,
R. Monasson

Lecture notes from the Les Houches Summer School 2022; To Appear in SciPost Phys. Lect. Notes (2023)

**
[147] Transition paths in Potts-like energy landscapes: General properties and application to protein sequence models
**

E. Mauri, S. Cocco, R. Monasson

Phys. Rev. E 108, 024141 (2023)

**
[146] Infer global, predict local: quantity-relevance trade-off in protein fitness predictions from sequence data
**

L. Posani, F. Rizzato, R. Monasson, S. Cocco

PLoS Computational Biology 19:e1011521 (2023)

**
[145] A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity
**

B. Bravi, A. Di Gioacchino, J. Fernandez de Cossio Diaz, A. Walczak, T. Mora, S. Cocco, R. Monasson

eLife 12:e85126 (2023)

**
[144] Evolutionary Dynamics of a Lattice Dimer: a Toy Model for Stability vs. Affinity Trade-offs in Proteins
**

E. Loffredo, E. Vesconi, R. Razban, O. Peleg, E. Shakhnovich, S. Cocco, R. Monasson

J. Phys. A 56 455002 (2023); Special issue on Random Landscapes and
Dynamics in Evolution, Ecology and Beyond

**
[143] Repeats Mimic Immunostimulatory Viral Features Across a Vast
Evolutionary Landscape
**

P. Sulc, A. Di Gioacchino, A. Solovyov, S.A. Marhon, S. Sun, H.T. Lindholm, R. Chen, A. Hosseini, H. Jiang, B.H. Li, P. Mehdipour,
O. Abdel-Wahab, N. Vabret, K. LaCava, D. De
Carvahlo, R. Monasson, S. Cocco, B.D. Greenbaum

submitted for publication (2023)

**
[142] Disentangling representations in Restricted Boltzmann Machines without adversaries
**

J. Fernandez-de-Cossio-Diaz, S. Cocco, R. Monasson

Physical Review X 13, 021003 (2023)

**
[141] Mutational paths in protein-sequence landscapes: from sampling to mean-field characterization
**

E. Mauri, S. Cocco, R. Monasson

Physical Review Letters 130, 158402 (2023)

**
[140] Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies
**

C. Malbranke, D. Bikard, S. Cocco, R. Monasson, J. Tubiana

Current Opinion in Structural Biology 80:102571 (2023)

**
[139] Emergence of time persistence in a data-driven neural network model
**

S. Wolf, G. Le Goc, S. Cocco, G. Debregeas, R. Monasson

eLife 12:e79541 (2023)

**
[138] Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
**

A. Di Gioacchino, J. Procyk, M. Molari, J.S. Schreck, Y. Zou, Y. Liu, R. Monasson, S. Cocco, P. Sulc

PLoS Comp. Bio. 18(9):e1010561 (2022)

**
[137] Neoantigen quality predicts immunoediting in pancreatic cancer survivors
**

M. Luksza, Z.M. Sethna, L.A. Rojas, J. Lihm, B. Bravi, Y. Elhanati, K. Soares, M. Amisaki, D. Hoyos, A. Dobrin, P. Guasp, A. Zebboudj, R. Yu, A.K. Chandra, T. Waters, Z. Odgerel, J. Leung, R. Kappagantula, A. Makohon-Moore, A. Johns, A. Gill, M. Gigoux, J. Wolchok, T. Merghoub, M. Sadelain, E. Patterson, C. Iacobuzio-Donahue, R. Monasson, T. Mora, A.M. Walczak, S. Cocco, B.D. Greenbaum, V.P. Balachandran

Nature 606, 389-395 (2022)

**
[136] Optimal regularizations for data generation with probabilistic
graphical models
**

A. Fanthomme, F. Rizzato, S. Cocco, R. Monasson

J. Stat. Mech. 053502 (2022)

**
[135] Barriers and dynamical paths in alternating Gibbs sampling of
restricted Boltzmann machines
**

C. Roussel, S. Cocco, R. Monasson

Physical Review E 104, 034109 (2021)

**
[134] Inferring epistasis from genomic data with comparable mutation
and outcrossing rate
**

H-L. Zeng, E. Mauri, V. Dichio, S. Cocco, R. Monasson, E. Aurell

J. Stat. Mech. 083501 (2021)

**
[133] A synaptic novelty signal in the dentate gyrus supports switching
hippocampal attractor networks from generalization to discrimination
**

R. Gomez-Ocadiz, M. Trippa, L. Posani, S. Cocco, R. Monasson, C. Schmidt-Hieber

Nature Communications 13, 4122 (2022)

**
[132] Probing T-cell response by sequence-based probabilistic modeling
**

B. Bravi, V.P. Balachandran, B.D. Greenbaum, A.M. Walczak, T. Mora, R.
Monasson, S. Cocco

PLoS Computational Biology 17(9): e1009297 (2021)

**
[131] Survival probability and size of lineages in antibody affinity
maturation
**

M. Molari, R. Monasson, S. Cocco

Physical Review E 103, 052413 (2021)

**
[130] Improving sequence-based modeling of protein families using
secondary structure quality assessment
**

C. Malbranke, D. Bikard, S. Cocco, R. Monasson

Bioinformatics, btab442 (2021)

**
[129] Low-Dimensional Manifolds Support Multiplexed Integrations
in Recurrent Neural Networks
**

A. Fanthomme, R. Monasson

Neural Computation 33, 1-50 (2021)

**
[128] Gaussian Closure Scheme in the Quasi-Linkage Equilibrium Regime
of Evolving Genome Populations
**

E. Mauri, S. Cocco, R. Monasson

Europhysics Letters 132, 56001 (2020)

**
[127] The heterogeneous landscape and early evolution of
pathogen-associated CpG dinucleotides in SARS-CoV-2
**

A. Di Gioacchino, P. Sulc, A.V. Komarova, B.D. Greenbaum, R. Monasson, S. Cocco

Molecular Biology and Evolution 38, 2428-2445 (2021)

**
[126] RBM-MHC: a semi-supervised machine-learning method for
sample-specific prediction of antigen presentation by
HLA-I alleles
**

B. Bravi, J. Tubiana, S. Cocco, R. Monasson,
T. Mora, A.M. Walczak

Cell Systems 12, 1-8 (2021)

**
[125] An evolution-based model for designing chorismate mutase enzymes
**

W.P. Russ, M. Figliuzzi, C. Stocker,
P. Barrat-Charlaix, M. Socolich, P. Kast, D.
Hilvert, R. Monasson, S. Cocco, M. Weigt, R. Ranganathan

Science 369, 440-5 (2020)

**
[124] Quantitative modeling of the effect of antigen dosage on B-cell
affinity distributions in maturating germinal centers
**

M. Molari, K. Eyer, J. Baudry, S. Cocco, R. Monasson

eLife 2020 9:e55678 (2020)

**
[123] Spectrum of multispace Euclidean Random Matrices
**

A. Battista, R. Monasson

Physical Review E 101, 052133 (2020)

**
[122] 'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of
continuous symmetries in the weight space
**

M. Harsh, J. Tubiana, S. Cocco, R. Monasson

J. Phys. A 53, 174002 (2020)

**
[121] Capacity-resolution trade-off in the optimal learning of multiple low-dimensional manifolds by attractor neural networks
**

A. Battista, R. Monasson

Physical Review Letters 124, 048302 (2020),
(supplemental material)

**
[120] Parameters and determinants of responses to selection in antibody libraries
**

S. Schulz, S. Boyer, M. Smerlak, S. Cocco, R. Monasson, C. Nizak, O. Rivoire

PLoS Comp. Biol. 17:e1008751 (2021)

**
[119] Inference of compressed Potts graphical models
**

F. Rizzato, A. Coucke, E. de Leonardis, J.P. Barton, J. Tubiana, R. Monasson, S. Cocco

Physical Review E 101, 012309 (2020)

**
[118] Can grid cell ensembles represent multiple spaces?
**

D. Spalla, A. Dubreuil, S.Rosay, R. Monasson, A. Treves

Neural Computation 31, 2324-2347 (2019)

**
[117] Physique statistique et apprentissage machine : une methode et trois exemples
**

R. Monasson

Gretsi conference (2019)

**
[116] Learning Compositional Representations of
Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice
Proteins**

J. Tubiana, S. Cocco, R. Monasson

Neural Computation 31(8), 1671-1717 (2019)

**
[115] Integration and multiplexing of positional and contextual information by the hippocampal network
**

L. Posani, S. Cocco, R. Monasson

PLoS Computational Biology 14: e1006320 (2018).

**
[114] Learning protein constitutive motifs from sequence data
**

J. Tubiana, S. Cocco, R. Monasson

eLife 2019;8:e39397 (2019).
See also
the press release.

**
[113] Adaptation of olfactory receptor abundances for efficient coding
**

T. Tesileanu, S. Cocco, R. Monasson, V. Balasubramanian

eLife 2019;8:e39279 (2019).
See also
the press release.

**
[112] Statistical Physics and Representations in Real and Artificial Neural Networks
**

S. Cocco, R. Monasson, L. Posani, S. Rosay, J. Tubiana

Lectures Notes of Fundamental Problems in Statistical Physics XIV, Physica A 504, 45-76 (2018).

**
[111] Innovation rather than improvement: a solvable high-dimensional model highlights the limitations of scalar fitness
**

M. Tikhonov, R. Monasson

Journal of Statistical Physics 172, 74-104 (2018)

**
[110] Functional Networks from Inverse Modeling of Neural Population Activity
**

S. Cocco, R. Monasson, L. Posani, G. Tavoni

Current Opinion in Systems Biology 3, 103-110 (2017)

**
[109] Evolutionary constraints on coding sequences at the nucleotidic level: a statistical physics approach
**

D. Chatenay, S. Cocco, B. Greenbaum, R. Monasson, P. Netter

chapter of "Evolutionary Biology: Self/Nonself Evolution, Species and Complex Traits Evolution, Methods and Concepts", Editor P. Pontarotti (2017).

**
[108] Sensorimotor computation underlying phototaxis in zebrafish
**

S. Wolf, A. Dubreuil, T. Bertoni, U.L. Bohm, V. Bormuth, R. Candelier, S. Karpenko, R. Monasson, G. Debregeas.

Nature Communications 8, 651 (2017).

**
[107] Inverse Statistical Physics of Protein Sequences: A Key Issues Review
**

S. Cocco, C. Feinauer, M. Figliuzzi, R. Monasson, M. Weigt

Reports on Progress in Physics 81, 032601 (2018).

**
[106] Inference of principal components of noisy correlation matrices with prior information
**

R. Monasson

Proceedings of the 50th Asilomar Conference on Signals, Systems, Computers, 10.1109/ACSSC.2016.7869001 (2017).

**[105] Emergence of compositional representations in restricted Boltzmann machines
**

J. Tubiana, R. Monasson

Physical Review Letters 118, 138501 (2017).
(supplemental material,
simulations Gaussian RBM,
simulations ReLU RBM)

**[104] A collective phase in resource competition in a highly diverse ecosystem
**

M. Tikhonov, R. Monasson

Physical Review Letters 118, 048103 (2017).
(supplemental material)

**[103] Functional connectivity models for brain state identification: application to decoding of spatial representations from hippocampal CA1 and CA3 recordings
**

L. Posani, S. Cocco, K. Jezek, R. Monasson

J. Comp. Neurosci. 43, 17-33 (2017).

**[102] Direct coevolutionary couplings reflect biophysical residue interactions in proteins
**

A. Coucke, G. Uguzzoni, F. Oteri, S. Cocco, R. Monasson, M. Weigt

J. Chem. Phys. 145, 174102 (2016).

**[101] Neural assemblies revealed by inferred connectivity-based models of prefrontal
cortex recordings
**

G. Tavoni, S. Cocco, R. Monasson

J. Comp. Neurosci. 41, 269-293 (2016).

**[100] Benchmarking inverse statistical approaches for protein structure and design with exactly solvable
models
**

H. Jacquin, A. Gilson, E. Shakhnovich, S. Cocco, R. Monasson

PLoS Comput Biol 12: e1004889 (2016)

**[99] On the entropy of protein families
**

J.P. Barton, A.K. Chakraborty, S. Cocco, H. Jacquin, R. Monasson

Journal of Statistical Physics 162, 1267-1293 (2016)

**[98] Learning probability distributions from smooth observables and the maximum entropy principle: some remarks
**

T. Obuchi, R. Monasson

Journal of Physics Conf. Ser. 638, 012018 (2015)

**[97] Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction
**

E. De Leonardis, S. Lutz, S. Ratz, S. Cocco, R. Monasson, A. Schug, M. Weigt

Nucleic Acid Research, doi: 10.1093/nar/gkv932 (2015)
(supplemental
text and figures and
supplemental
material)

**[96] Distinguishing the Immunostimulatory Properties of Non-coding RNAs Expressed in Cancer Cells
**

A. Tanne, L. Muniz, A. Puzio-Kuter, K. Leonova, A. Gudkov, D. Ting, R. Monasson, S. Cocco, A. Levine, N. Bhardwaj, B. Greenbaum

Proc. Natl. Acad. Sci. USA 112, 15154-15159 (2015), doi: 10.1073/pnas.1517584112
(supplementary methods and experiments)

see also ** Immunostimulatory noncoding RNAs**, in Highlights (Medical Sciences)

and the commentary **Silent pericentromeric repeats speak out** by S.T. Younger and J.L. Rinn.

**[95] Transitions between spatial attractors in place-cell models
**

R. Monasson, S. Rosay

Physical Review Letters 115, 09810 (2015)
(supplemental
material text and movie)

**[94] Learning probabilities from random observables in high dimensions: the maximum entropy distribution and others
**

T. Obuchi, S. Cocco, R. Monasson

Journal of Statistical Physics 161, 598-632 (2015)

**[93] Estimating the principal components of correlation matrices from all their empirical eigenvectors
**

R. Monasson, D. Villamaina

Europhysics Letters 112, 50001 (2015) - Editor's choice and EPL Highlights 2015

**[92] Large Pseudo-Counts and L2-Norm Penalties Are Necessary for the
Mean-Field Inference of Ising and Potts Models
**

J.P.Barton, S. Cocco, E. De Leonardis, R. Monasson

Physical Review E 90, 012132 (2014)

**[91] Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity
**

G. Tavoni, U. Ferrari, F.P. Battaglia, S. Cocco, R. Monasson

Network Neuroscience 1, 275-301 (2017)
(supporting information)

**[90] Stochastic Ratchet Mechanisms for Replacement of Proteins Bound to DNA
**

S. Cocco, J.F. Marko, R. Monasson

Physical Review Letters 112, 238101 (2014)
(supplemental material)

**[89] A Quantitative Theory of Entropic Forces Acting on Constrained Nucleotide Sequences Applied to Viruses
**

B. Greenbaum, S. Cocco, A. Levine, R. Monasson

Proc. Natl. Acad. Sci. USA 111, 5054-5059 (2014)

**[88] Crosstalk and transitions between multiple spatial maps in an
attractor neural network model of the hippocampus: Collective motion of the activity
**

R. Monasson, S. Rosay

Physical Review E 89, 032803 (2014)

**[87] Trend or Fluctuations? Analysis and design of population dynamics
measurements in replicate ecosystems.
**

D.R. Hekstra, S. Cocco, R. Monasson, S. Leibler

Physical Review E 88, 062714 (2013)
(supplementary information)

**[86]
Reconstruction and identification of DNA sequence landscapes from unzipping experiments at equilibrium**

C. Barbieri, S. Cocco, T. Jorg, R. Monasson

Biophysical Journal 106, 430-9 (2014)
(supporting material)

**[85]
Hopfield-Potts patterns for covariation in protein families: calculation and statistical error bars**

S. Cocco, R. Monasson, M. Weigt

J. Phys. Conference Series 473, 012010 (2013)

**[84]
From principal component to direct coupling analysis of coevolution in
proteins: Low-eigenvalue modes are needed for structure prediction
**

S. Cocco, R. Monasson, M. Weigt

PLoS Comput Biol 9, E1003176 (2013)
(supplementary information)

**[83]
Crosstalk and transitions between multiple spatial maps in an
attractor neural network model of the hippocampus: Phase diagram
**

R. Monasson, S. Rosay

Physical Review E 87, 062813 (2013)

see also ** Knowing Your Place**, D. Voss,
Synopsis in Physics.

**[82]
Lorenzo Saitta, Attilio Giordana, Antoine Cornuejols: Phase Transitions in Machine Learning
**

R. Monasson

J. Stat. Phys. 149, 1161 (2012)

**[81]
Adaptive cluster expansion for the inverse Ising problem: convergence,
algorithm and tests
**

S. Cocco, R. Monasson

J. Stat. Phys. 147, 252 (2012)

**[80]
High-Dimensional Inference with the generalized Hopfield Model:
Principal Component Analysis and Corrections.
**

S. Cocco, R. Monasson, V. Sessak

Physical Review E 83, 051123 (2011)

**[79]
On the trajectories and performance of Infotaxis,
an information-based greedy search algorithm.
**

C. Barbieri, S. Cocco, R. Monasson

Europhysics Letters 94, 20005 (2011)

** [78] Adaptive cluster expansion for inferring Boltzmann machines with noisy data.
**

S. Cocco, R. Monasson

Physical Review Letters 106, 090601 (2011)
(supplementary information)

**[77]
Fast Inference of Interactions in Assemblies of Stochastic
Integrate-and-Fire Neurons from Spike Recordings
**

R. Monasson, S. Cocco

Journal of Computational Neuroscience 31, 199-227 (2011)

**[76]
Theory of spike timing-based neural classifiers.
**

R. Rubin, R. Monasson, H. Sompolinsky

Physical Review Letters 105, 218102 (2010)
(supplementary information)

**[75]
Inference of a random potential from random walk realizations:
formalism and application to the one-dimensional Sinai model with a drift
**

S. Cocco, R. Monasson

Journal of Physics: Conference Series 197, 012005 (2009)

** [74] Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods.
**

S. Cocco, S. Leibler, R. Monasson

Proc. Natl. Acad. Sci. USA 106, 14058 (2009)
(supplementary information)

**[73]
Dynamical modelling of molecular constructions and setups for DNA
unzipping.**

C. Barbieri, S. Cocco, R. Monasson, F. Zamponi

Phys. Biol. 6, 025003 (2009)

**[72]
Small-correlation expansions for the inverse Ising problem.**

V. Sessak, R. Monasson

Journal of Physics A 42, 055001 (2009)

**[71]
A review of the statistical mechanics approach to random optimization problems.**

F. Altarelli, R. Monasson, G. Semerjian, F. Zamponi

Handbook of Satisfiability, edited by Armin Biere, Marijn Heule, Hans van Maaren, and Toby Walsh, IOS Press (2009)

**[70]
Reconstructing a random potential from its random walks.**

S. Cocco, R. Monasson.

Europhysics Letters 81, 20002 (2008)

**[69]
Relationship between clustering and algorithmic phase transitions in the
random k-XORSAT model and its NP-complete extensions.**

F. Altarelli, R. Monasson, F. Zamponi.

Journal of Physics: Conference Series 95, 012013 (2007)

**[68]
Von Neumann's expanding model on random graphs.**

A. De Martino, C. Martelli, R. Monasson, I. Perez Castillo

J. Stat. Mech. P05012 (2007)

**[67]
Can rare SAT formulae be easily recognized? On the efficiency of message-passing algorithms for K-SAT at large clause-to-variable ratios.**

F. Altarelli, R. Monasson, F. Zamponi.

Journal of Physics A 40, 867-886 (2007)

**[66]
Inferring DNA sequences from mechanical unzipping data:
the large-bandwidth case.**

V. Baldazzi, S. Bradde, S. Cocco, E. Marinari, R. Monasson

Phys. Rev. E 75, 011904 (2007).

**[65]
Introduction to Phase Transitions in Random Optimization Problems**

R. Monasson

Lecture Notes of Les Houches Summer School, Elsevier (2006)

**[64] The mechanical opening of DNA and the sequence content
**

S. Cocco, R. Monasson

AIP Conference Proceedings, vol 851, p 50 (2006)

**[63]
Inference of DNA sequences from mechanical unzipping
experiments: an ideal-case study**

V. Baldazzi, S. Cocco, E. Marinari, R. Monasson

Phys. Rev. Lett. 96, 128102 (2006).

**[62]
Criticality and Universality in the Unit-Propagation Search Rule.**

C. Deroulers, R. Monasson.

Eur. Phys. J. B 49, 339 (2006)

** [61] An algorithm for counting circuits: application to real-world and random graphs.
**

E. Marinari, R. Monasson, G. Semerjian.

Europhysics Letters 73, 8 (2006).

**[60]
Multiple aspects of DNA and RNA: from biophysics to bioinformatics.
**

D. Chatenay, S. Cocco, R. Monasson, D. Thieffry, J. Dalibard (eds)

Lecture Notes of Les Houches Summer School, Elsevier (2005)

**[59]
A generating function method for the average-case analysis of DPLL.**

R. Monasson.

Lecture Notes in Computer Science 3624, 402-413 (2005)

**[58]
Restarts and exponential acceleration of random 3-SAT
instances resolutions: a large deviation analysis of the
Davis-Putnam-Loveland-Logemann algorithm.
**

S. Cocco, R. Monasson.

Annals of Mathematics and Artificial Intelligence 43, 153-172 (2005)

**[57]
Critical behaviour of combinatorial search algorithms, and
the unitary-propagation universality class.**

C. Deroulers , R. Monasson.

Europhys. Lett. 68, 153 (2004)

**[56]
Circuits in random graphs: from local trees to global loops.**

E. Marinari, R. Monasson.

J. Stat. Mech. P09004 (2004).

**[55]
On large-deviations properties of Erdos-Renyi random graphs.**

A. Engel, R. Monasson, A.K. Hartmann.

J. Stat. Phys. 117, 387 (2004).

**[54]
Heuristic average-case analysis of the backtrack resolution
of random 3-Satisfiability instances.**

S. Cocco, R. Monasson.

Theoretical Computer Science A 320, 345 (2004).

**[53]
A study of Pure Random Walk on Random Satisfiability problems with "physical" methods
**

G. Semerjian, R. Monasson.

Proceedings of the SAT 2003 conference, E. Giunchiglia and A. Tachella eds.,
Lecture Notes in Computer Science 2919, 120 (2004)

** [52] Field theoretic approach to metastability in the contact process.
**

C. Deroulers, R. Monasson.

Phys. Rev. E 69, 016126 (2004).

** [51]
On the analysis of backtrack procedures for the coloring of random graphs.
**

R. Monasson.

Chapter for "Complex Networks" edited by E. Ben-Naim, H. Frauenfelder,
Z. Torczkai, Springer-Verlag (2004)

**[50] Approximate analysis of search algorithms with ``physical'' methods.
**

S. Cocco, R. Monasson, A. Montanari, G. Semerjian.

Chapter for "Phase transitions and Algorithmic complexity"
edited by G. Istrate, C. Moore, A. Percus (2004)

** [49] Analysis of backtracking procedures for random decision problems
**

S. Cocco, L. Ein-Dor, R. Monasson.

Chapter for "New optimization algorithms in physics"
edited by A. Hartmann, H. Rieger, Wiley (2004)

**
[48] The dynamics of proving uncolourability of large random graphs.
I. Symmetric Colouring Heuristic.
**

L. Ein-Dor, R. Monasson.

J. Phys. A 36, 11055 (2003)

**
[47] Relaxation and Metastability in a local search procedure for the
random satisfiability problem.**

G. Semerjian, R. Monasson.

Phys. Rev. E 67, 066103 (2003)

**
[46] Force-extension behavior of folding polymers.**

S. Cocco, J.F. Marko, R. Monasson, A. Sarkar, J. Ya.

Eur. Phys. J. E 10, 249 (2003).

**
[45] Slow nucleic acid unzipping kinetics from sequence-defined barriers.**

S. Cocco, R. Monasson, J.F. Marko.

Eur. Phys. J. E 10, 153 (2003).

**
[44] Rigorous decimation-based construction of ground pure states
for spin glass models on random lattices.**

S. Cocco, O. Dubois, J. Mandler, R. Monasson.

Phys. Rev. Lett. 90, 047205 (2003)

**
[43] Exponentially hard problems are sometimes polynomial,
a large deviation analysis of search
algorithms for the random Satisfiability problem,
and its application to stop-and-restart resolutions.
**

S. Cocco, R. Monasson.

Phys. Rev. E 66, 037101 (2002)

**
[42] Theoretical models for single-molecule DNA and RNA experiments:
from elasticity to unzipping.**

S. Cocco, J.F. Marko, R. Monasson.

C.R. Physique 3, 569-584 (2002)

**
[41] Phase transitions and Complexity in computer science:
An overview of the statistical physics approach to the random
satisfiability problem. **

G. Biroli, S. Cocco, R. Monasson.

Physica A 306, 381-394 (2002).

**
[40] Unzipping dynamics of long DNAs.**

S. Cocco, R. Monasson, J.F. Marko.

Phys. Rev. E 66, 051914 (2002).

**
[39] Force and kinetic barriers to initiation of DNA unzipping.
**

S. Cocco, R. Monasson, J. Marko.

Phys. Rev. E 65, 041907 (2002).

**
[38] A la rescousse de la complexité calculatoire. **

S. Cocco, O. Dubois, J. Mandler, R. Monasson.

Pour la Science, Mai 2002, Editions Belin.

**
[37] Statistical physics analysis of the computational complexity of solving random
satisfiability problems using branch and bound search algorithms.
**

S. Cocco, R. Monasson.

Eur. Phys. J. B 22, 505 (2001).

**
[36] Trajectories in phase diagrams, growth processes and computational
complexity: how search algorithms solve the 3-Satisfiability problem.**

S. Cocco, R. Monasson.

Phys. Rev. Lett. 86, 1654 (2001).

**
[35] Force and kinetic barriers in unzipping of DNA.
**

S. Cocco, R. Monasson, J. Marko.

Proc. Natl. Acad. Sci. USA 98, 8608 (2001).

**
[34] Statistical mechanics methods and phase transitions
in optimization problems. **

O. Martin, R. Monasson, R. Zecchina.

Theoretical Computer Science 265, 3 (2001).

**
[33] Le temps d'un choix : transitions de phase et complexité en
informatique. **

G. Biroli, S. Cocco, R. Monasson.

Images de la Physique 2001, CNRS Editions.

**
[32] Theoretical study of collective modes in DNA at ambient
temperature.
**

S. Cocco, R. Monasson.

J. Chem. Phys. 112, 100 (2000)

**
[31] From inherent structures to pure states: some simple remarks and
examples. **

G. Biroli, R. Monasson.

Europhys. Lett. 50, 155 (2000).

**
[30] A variational description of the ground state structure
in random satisfiability problems. **

G. Biroli, R. Monasson, M. Weigt.

Eur. Phys. J. B 14, 551 (2000).

**
[29] Statistical Mechanics of Torque Induced Denaturation of DNA.
**

S. Cocco, R. Monasson.

Phys. Rev. Lett. 83, 5178 (1999)

**
[28] 2+p-SAT: Relation of Typical-Case Complexity to the Nature of
the Phase Transition.
**

R. Monasson, R. Zecchina, S. Kirkpatrick, B. Selman,
L. Troyansky.

Random Structure and Algorithms 15, 414 (1999).

**
[27] Determining computational complexity from characteristic `phase
transitions'.**

R. Monasson, R. Zecchina, S. Kirkpatrick, B. Selman,
L. Troyansky.

Nature 400, 133 (1999).

see also **Solving problems in finite time**,
P.W. Anderson,
Nature 400, 115 (1999).

**
[26] Diffusion, localization and dispersion relations on
'small-world' lattices.**

R. Monasson.

Eur. Phys. J. B 12, 555 (1999)

**
[25] A single defect approximation for localized states on random lattices.**

G. Biroli, R. Monasson.

J. Phys. A 32, L255 (1999).

**
[24] Optimization problems and replica symmetry breaking in finite
connectivity spin-glasses.**

R. Monasson.

J. Phys. A 31, 515 (1998).

**
[23] Some remarks on hierarchical replica symmetry breaking in
finite-connectivity systems.**

R. Monasson.

Phil. Mag. B 77, 1515 (1998).

**
[22] Relationship between long timescales and the static free-energy
in the Hopfield model. **

G. Biroli, R. Monasson.

J. Phys. A 31, L391 (1998).

**
[21] Tricritical points in random combinatorics: the 2+p-SAT case.**

R. Monasson, R. Zecchina.

J. Phys. A 31, 9209 (1998).

**
[20] Entropy of particles packings : an illustration on a toy model.**

R. Monasson, O. Pouliquen.

Physica A 236, 395 (1997).

**
[19] Statistical mechanics of the random K-SAT model.**

R. Monasson, R. Zecchina.

Phys. Rev. E 56, 1357 (1997).

**
[18] Phase transition and search cost in the 2+p-sat problem.
**

R. Monasson, R. Zecchina, S. Kirkpatrick, B. Selman,
L. Troyansky.

Proceedings of PhysComp 96, T. Toffoli, M. Biafore, J. Leao eds.,
Boston (1996).

**
[17] Entropy of the K-satisfiability problem.
**

R. Monasson, R. Zecchina.

Phys. Rev. Lett. 76, 3881 (1996)

**
[16] Analytical and numerical study of internal representations in
multilayer neural networks with binary weights. **

S. Cocco, R. Monasson, R. Zecchina.

Phys. Rev. E 54, 717 (1996).

**
[15] Learning and generalization theories of large committee machines.**

R. Monasson, R. Zecchina.

Modern Physics Letters B 9, 1897 (1996).

**
[14] A mean--field hard spheres model of glass. **

L. Cugliandolo, J. Kurchan, R. Monasson, G. Parisi.

J. Phys. A 29, 1347 (1996).

**
[13] Replica structure of one-dimensional Ising systems.**

M. Weigt, R. Monasson.

Europhys. Lett. 36, 209 (1996).

**
[12] Structural glass transition and the entropy of
the metastable states.**

R. Monasson.

Phys. Rev. Lett. 75, 2847 (1995).

**
[11] How (super-)rough is the glassy phase of a crystalline
surface with a disordered substrate?**

E. Marinari, R. Monasson, J. Ruiz.

J. Phys. A 28, 3975 (1995).

**
[10] Weight space structure and internal representations:
a direct approach to learning and generalization
in multilayer neural networks. **

R. Monasson, R. Zecchina.

Phys. Rev. Lett. 75, 2432 (1995);
Erratum Phys. Rev. Lett. 76, 2205 (1996).

**
[9] Glassy transition in the three-dimensional random field
Ising model.**

M. Mezard, R. Monasson.

Phys. Rev. B 50, 7199 (1994).

**
[8] A storage algorithm for two-layered neural networks.**

R. Monasson.

Int. J. Neur. Syst. 5, 153 (1994) (reprint available on request).

**
[7] Domains of solutions and replica symmetry breaking in
multilayer neural networks. **

R. Monasson, D. O'Kane.

Europhys. Lett. 27, 85 (1994) (reprint available on request).

**
[6] Memory retrieval in optimal subspaces.
**

G. Boffetta, R. Monasson, R. Zecchina.

Int. J. Neur. Syst. 3, 71 (1993) (reprint available on request).

**
[5] Symmetry breaking in non-monotonic neural networks.
**

G. Boffetta, R. Monasson, R. Zecchina.

J. Phys. A 26, L507 (1993).

**
[4] Storage of spatially correlated patterns in auto-associative
memories.
**

R. Monasson.

J. Physique I 3, 1141 (1993).

**
[3] Properties of neural networks storing spatially correlated
patterns.
**

R. Monasson.

J. Phys. A 25, 3701 (1992).

**
[2] On the capacity of neural networks with binary weights.
**

I. Kocher, R. Monasson.

J. Phys. A 25, 367 (1992).

**
[1] Generalization error and dynamical effects in a
two-dimensional patches detector. **

I. Kocher, R. Monasson.

Int. J. Neur. Syst. 2, 115 (1991) (reprint available on
request).

**
[0] !Colour
**

R. Monasson.

Hebdogiciel 111 (1985),
Hebdogiciel 112 (1985),